The present invention relates to time series data processing, and more specifically, to storing in a database time series data from sensors.
In various industrial applications, it is needed to process massive time series data (such as stock price fluctuation, temperature change, and the like) from a large number of sensors. Time series data comprises timestamps and values associated with the timestamps, for example, comprising sampling time and sample values from sensors. In applications such as the energy system, intelligent power grid, etc., millions of sensors are usually deployed. These sensors generate massive time series data that are required to be persistently stored in a database for query. Before persistently storing time series data, it is first required to receive time series data in real-time from sensors and temporarily store the received time series data in a database of a temporary memory. The time series data are generally organized and stored temporarily according to individual sensors. This approach, although straightforward, has low utilization efficiency of buffers, and in particular so when processing massive time series data from a considerable number of low-frequency sampling sensors.